Artificial Intelligence (incl. Robotics)

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ARTICULATING UNEVEN REGIONAL DEVELOPMENT: ARTIFICIAL INTELLIGENCE AS A TOOL IN DEVELOPMENT PLANNING

ARTICULATING UNEVEN REGIONAL DEVELOPMENT: ARTIFICIAL INTELLIGENCE AS A TOOL IN DEVELOPMENT PLANNING

1995; Nilsson, 1998). The artificial intelligence technique chosen for our study here is the K-SOM, an unsupervised learning technique that clusters data based on a distance function without any a priori information on the number of clusters. The (artificial) intelligence of the algorithm is that it discerns something similar to what the human brain sees in the dataset. In the present context, the algorithm is able to group or cluster regions with similar combinations of indicators based on information within the data set itself. Once again, a technical understanding of the K-SOM algorithm is beyond the scope of this paper. Interested readers may refer to Beale and Jackson (1990), Kohonen (1990), Aleksander and Morton (1995), Kaski and Kohonen (1996), Beveridge (1996), Frohlich (1999), Germano (1999), and Deboeck (2000). A brief description the K-SOM technique is presented in Appendix 2.
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21 Lee mas

Artificial intelligence impact on the legal sphere

Artificial intelligence impact on the legal sphere

The research is devoted to the analysis of the opportunities and prospects for the implementation of artificial intelligence in the legal system. The purpose of the study is to analyze the advantages and disadvantages, opportunities and limits of introducing digital technologies into the legal environment. The study analyzed the points of view about the theme of research among the national and foreign scientists, assessed the influence of the artificial intelligence influence on the legal sphere. We also studied the opportunities for regulation connected with digital technologies in current Russian legislation, the advantages, and disadvantages of new categories in the Civil Code of the Russian Federation. We presented examples of the negative impact of legal vacuum on the law enforcement practice and suggested the ways for its overcoming. The current trends in the incorporation of digital technologies into the legal sphere of the Russian Federation and other countries were also studied.
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7 Lee mas

Micromechanics as a testbed for artificial intelligence methods evaluation

Micromechanics as a testbed for artificial intelligence methods evaluation

Abstract. Some of the artificial intelligence (AI) methods could be used to improve the performance of automation systems in manufacturing processes. However, the application of these methods in the industry is not widespread because of the high cost of the experiments with the AI systems applied to the conventional manufacturing systems. To reduce the cost of such experiments, we have developed a special micromechanical equipment, similar to conventional mechanical equipment, but of a lot smaller overall sizes and therefore of lower cost. This equipment can be used for evaluation of different AI methods in an easy and inexpensive way. The methods that show good results can be transferred to the industry through appropriate scaling. This paper contains brief description of low cost microequipment prototypes and some AI methods that can be evaluated with mentioned prototypes.
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10 Lee mas

Credit Card Fraud Detection using Artificial Intelligence

Credit Card Fraud Detection using Artificial Intelligence

Every year, billions of dollars are lost due to credit card fraud, causing huge losses for users and the financial industry. This kind of illicit activity is perhaps the most common and the one that causes most concerns in the finance world. In recent years great attention has been paid to the search for techniques to avoid this significant loss of money. In this degree project, we address credit card fraud by using an imbalanced dataset that contains transactions made by credit card users. Our Q-Credit Card Fraud Detector system classifies transactions into two classes: genuine and fraudulent and is built with artificial intelligence techniques comprising Deep Learning, Autoencoder, and Neural Agents, elements that acquire their predicting abilities through a Q-learning algorithm. Our computer simulation experiments show that the assembled model can produce quick responses with a remarkable accuracy value (98.1) and high performance in fraud classification, which is necessary for this model to be reliable and have relevance in future research.
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72 Lee mas

Automatic detection of signals by using artificial intelligence techniques

Automatic detection of signals by using artificial intelligence techniques

According to the detection problems to be solved in the thesis, some premises are set. Syn- thetic radar scans are generated in simulated environments having: time-correlation between consecutive cells; and constant clutter properties (skewness parameter) inside a scan, but vari- able scan-to-scan. Targets of different sizes and shapes are included in the synthetic radar scans. Different radar environments have been considered in the thesis by using the statistical parame- ters of sea, sea-ice and ground clutters reported in the literature. From these environments, it is observed that the clutter statistics are different each other, making the problem of proposing a detector scheme able to work with high performance in different environments more complicated. For solving the detection problems this thesis deals with, Artificial Intelligence (AI) based detectors are proposed, and compared with commonly used detectors selected from the literature. The coherent detector set as reference is the target sequence known a priori (TSKAP) detector. The incoherent detector set as reference is based on constant false alarm rate (CFAR) techniques. From AI techniques, two feed-forward artificial neural networks (ANNs) strategies are selected: the multilayer perceptrons (MLPs) and the radial basis function ANNs (RBF-ANNs, also referred as RBFNs). By using these AI techniques, coherent and incoherent approaches are proposed. An additional contribution is made in the thesis by proposing new modes of selecting the cells to be processed. Thus, not only the commonly used non-delayed selection modes are used, but also additional delayed selection modes are studied. These proposed modes are based on 2-dimension selection templates, instead of the 1-dimension templates commonly used in CFAR detectors.
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285 Lee mas

Artificial intelligence and knowledge management

Artificial intelligence and knowledge management

persons and technologies, go altering their objectives and originating an endless cycle of renewal. Inside that cycle presents-itself of clear form the dizzy evolution of the technology of the data processing, that has amount of studies dedicated to the reproduction of abilities and human capacities such the manuals, as much as the intellectuals, that is the Artificial Intelligence (AI). The intelligence is more than the faculty of learn how, apprehend or understand, interpret and, mainly adapt itself to the situations.

9 Lee mas

TítuloNeurocybernetics and Artificial Intelligence

TítuloNeurocybernetics and Artificial Intelligence

The most basic level (where computational machines still do not appear, strictly, apart from as tools) is the level of the neurotransmitters, membrane phenomena and action potentials. Tools present in this level are Biochemistry and Biophysics. Then comes Biophysics of Neural codes and multiple codes, where this is a word used in neurophysiology to indicate multiplex. Then we move onto Biophysics and Signal Processing. We continue through sensorial codes, decodification in effec- tors - motor and glandular action - and the code of advanced peripheral neurons such as the ganglion cells in the retina. We are now in the realm of Signal Theory almost at the level of logic. Then, we have the neural net level, the interaction of input and output of the neurons themselves, and the coordination of the output -effectors. We are now at the level of the Language of Logic bordering on Symbolic Languages and, finally, we come to the central cortex neural code, the cooperative processes between masses of brain tissue, the extraction of Universals and the social processes of interaction between neuron masses. We are at the level of Symbolic language. The structure in levels is summarized in table 3. Upper square bounds the more classical formal tools of computational neuroscience. Lower square bounds techniques close to Artificial Intelligence tools.
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21 Lee mas

Juridical frame for the artificial intelligence applied to the robots like autonomous systems

Juridical frame for the artificial intelligence applied to the robots like autonomous systems

Además de estas corrientes que abogan tanto por un uso responsable de las tecnologías emergentes como por la conservación de la evolución de la naturaleza humana, hay que tener en cuenta que siendo estos enfoques ético-morales puntos de partida para analizar la Inteligencia Artificial y su influencia en el derecho, no hay que perder de vista la aplicabilidad de estas tecnologías que ya hoy en día se ven reflejados en varias áreas de estudio, por ejemplo entre las múltiples aplicaciones de la tecnología se encuentra la Cibernética que se ha entendido a través de la historia como la ciencia de dirigir, el arte de pilotar, la ciencia de dirección y transmisión y finalmente cibernética o como la define Rafael de Asís, entendiéndose como aquella que se ocupa de los sistemas de control y de comunicación de los seres vivos y en las máquinas, entre otras cosas ha contribuido a la creación de máquinas capaces de reaccionar y actuar con más precisión y rapidez que los seres vivos.
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22 Lee mas

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Optical connection (or lightpath) QoT estimation prior to de- ployment is particularly relevant in impairment-aware optical network design and operation. Azodolmolky et al. [67] presented a QoT estimator tool, the Q-Tool, which computes the associated Q-factors of a set of lightpaths, given a reference topology, by combining analytical models and numerical methods. These esti- mates are relatively accurate, but the necessary high computing time to perform the calculations makes this tool impractical in scenarios where time constraints are important. Several ap- proaches propose cognitive techniques to solve this drawback. As an example, Jiménez et al. [34] present a QoT estimator capable of exploiting previous experience and thus, provide with fast and correct decisions on whether a lightpath ful fi ls QoT requirements or not. It is based on case-based reasoning (CBR) [68], an arti fi cial intelligence mechanism that offers solutions to new problems by retrieving the most similar cases faced in the past whether by reusing them or after adapting them. Cases are retrieved from a knowledge base (KB), which can be static [34] or optimized with learning and forgetting techniques [35]. Results for CBR relying on an optimized KB show an excellent rate of successful classi fi cation of lightpaths into high/low QoT categories and more important, up to four orders of magnitude faster than the Q-Tool mentioned above. Furthermore, this study is experimentally demonstrated in a WDM 80 Gb/s PDM-QPSK testbed [36], where, even with a very small KB, very high rates of successful classi fi cations of lightpaths are achieved. One step further, and with the aim of further redu- cing the prediction time, Mata et al. [59] propose the use of an SVM classi fi er to predict if a lightpath ful fi ls QoT requirements or not. This classi fi er proves to be not only signi fi cantly faster but also more accurate than the proposal in Ref. [35].
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15 Lee mas

Game Artificial Intelligence: Challenges for the Scientific Community

Game Artificial Intelligence: Challenges for the Scientific Community

This paper aims at some interesting trends that seems to guide the future of videogames, and the challenges that they oer to academia, focusing on the ap- plication of articial intelligence and, more precisely, computational intelligence, i.e. bio-inspired optimization techniques and meta-heuristics [20]. We want to clarify that the universe of uses of optimization techniques on the development and game design is extremely broad and we do not pretend to make an ex- haustive tour on it in this paper; in fact we recommend the interested reader a reading of other papers that have been published in the literature and serve as a basis to learn about the state of the art [37,43]. We focused, instead, on certain research areas that will inuence signicantly in the creation of commer- cial games over the next decade, we refer to the procedural content generation, aective computing, which has an impact in player satisfaction and the creation of behaviors or strategies of decision making for non-playable characters (NPC).
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12 Lee mas

Investigaciones de Michele Taruffo y de la “Artificial Intelligence and Law”

Investigaciones de Michele Taruffo y de la “Artificial Intelligence and Law”

Afortunadamente, hay quienes han asumido frontalmente la misión de explorar las complejidades del razonamiento probatorio en el derecho. De entre quienes cultivan este tópico en nuestra tradición jurídica podemos mencionar a Daniel González Lagier, a Marina Gascón, a Jordi Ferrer y, por supuesto, a quien me parece que ha llevado la batuta, es decir, a Michele Taruffo. Así mismo, desde una perspectiva más tecnológica orientada al diseño de sistemas computacionales capaces de proporcionar asistencia de diversa índole a la profesión jurídica, podemos mencionar, en términos generales, al novedoso y transdisciplinario campo de la Artificial Intelligence and Law (o AI and Law), y particularmente, a Floris Bex.
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24 Lee mas

The future of artificial intelligence : mankind quest for replicating human intelligence

The future of artificial intelligence : mankind quest for replicating human intelligence

Association for the Advancement of Artificial Intelligence. A report to ARPA on twenty-first century intelligent systems, [1994?]. Eds. Barbara Grosz and Randall Davis. http://www.aaai.org/Library/Reports/arpa-report.php#3 (accessed November 16, 2013).

6 Lee mas

Educación Basada en la Web

Educación Basada en la Web

Reusser, Kurt. Tutoring Systems an Pedagogical Theory: Representational Tools for Understanding, Planning and Reflection in Problem Solving. En Computers as Cognitive Tools. Lajoie and Derry, ed. 1993. Ritter, S. Communication, Cooperation and Competition Among Multiple Tutor Agents. AI-ED97. Eighth World Conference on Artificial Intelligence in Education - Workshop V: Pedagogical Agents. 1997.

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To predict the future has always been the wish shared by a certain part of humanity. If that were possible, some would think that we could then change it in order to make it more acceptable, even happier. Predictive analysis is now being developed and based on Machine Learning (ML), composed of statistics and computer algorithms that make it possible to automate the construction of a prediction function using a set of observations called “learning sets”. The learning machine is supposed to make more effective predictions using Big Data technology. Our objective is to present what Machine Learning is, to compare human intelligence with artificial intelligence, and to explain how the machine learning could be used in predicting the success of adult learning (> 21 years), on digital networks (ODL, E-learning, MOOC, etc.).
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25 Lee mas

A copyright overview

A copyright overview

Copyright can apply to a wide range of artefacts, but of particular relevance to scholars are words (as in this article or in a book, and sometimes called “literary works”, though there is no requirement for literary merit), numbers (as in research data), images such as photographs, paintings and drawings, (also known as “artistic works”, though, again, there is no requirement that they must have artistic merit.) Another category is moving images (as in, for example, a film or video). There is also protection for sound recordings, for dramatic works (such as the text of a play) and for musical works. At the moment, copyright only protects products of the human mind, whether created in a few seconds or the result of years of effort. There is no copyright in things produced by nature, or by other living organisms. There is a question regarding things produced by computers; arguably purely computer-generated outputs where there has been no human input apart from the original programming effort (where there would be copyright in the software) do not enjoy copyright, but this is an area where novel copyright concepts might emerge in the future. I discuss copyright and artificial intelligence later in this article.
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13 Lee mas

Learning-Based Perception, Control, and Navigation for Autonomous Missions in Aerial Robotics

Learning-Based Perception, Control, and Navigation for Autonomous Missions in Aerial Robotics

Regarding the SAR paradigm, most of the works in the literature have focused on the task of exploration and target identification. Little attention has been put on the interaction with the target once it is detected. Early developments in high-level artificial intelligence applied to aerial robotics were introduced in Doherty and Rudol, 2007. In particular, UAV autonomous missions were implemented for the SAR of injured civilians, with robots being able to scan designated areas, trying to identify injured civilians and attempting to deliver medical and other supplies to identified victims in realistic urban scenarios. The authors in Waharte and Trigoni, 2010 proposed three strategies for search and rescue operations, which were studied and evaluated based on the time to find the target. In the greedy heuristics approach, each UAV moves to the neighboring cell based on the highest belief confidence. In the potential-based approach attractive and repul- sive potentials were created for navigating through the obstacles present in the scenario, searching for the target. The Partially Observable Markov Decision Process (POMDP) was studied for creating different observation models and a set of actions for each set of UAVs. The results obtained were presented only in simulation cases of study. The work by Tomic et al., 2012 introduced a modular and extensible software and hardware frame- work designed for the autonomous execution of SAR missions using aerial robots, which was successfully tested on a quadrotor platform. The intelligent transition from indoors to outdoors was addressed by switching between visual and laser odometry. However, while using multiple sensors (four cameras and a laser scanner), the proposed system did not feature any collision avoidance capabilities.
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234 Lee mas

Computational intelligence for simulating a LiDAR sensor

Computational intelligence for simulating a LiDAR sensor

In this chapter, an overview of some of the most commonly Computational Intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-art of Computational Intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific Artificial Intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine and a k-nearest neighbours. The results demonstrate the suitability of using Computational Intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.
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22 Lee mas

The Learning Machine and the Spaceship in the Garden  AI and the design of planetary 'nature'

The Learning Machine and the Spaceship in the Garden AI and the design of planetary 'nature'

Within this model, the systems failure comes not from the technology, but from the faulty interventions of its human operators. In the Tele-Garden, the first growing season was terminated by the over-watering of a single user who flooded the garden; in other instances, the garden would become overgrown without members banding together to cooperatively manage pruning, weeding and replanting across a larger area. In the contemporary incarnation of the Tele-Garden, the FarmBot automates many of these processes with “Sequences”, “Regimens”, and “Farmware”, including the use of image-recognition processes to detect weeds, which simplifies the production of coded cultivation sequences and management. Increasingly, the planetary scale of remote sensing and modelling projects and these small scale remote and robotic cultivation management processes are converging. At a planetary scale, Joppa, the Microsoft chief environment officer, presents a case that those judgments are also better removed from humans: “We need artificial intelligence to save us from ourselves”, he says, “My worry is AI won’t come soon enough 32 . More specifically, AI’s work will
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12 Lee mas

LAS PLATAFORMAS DIGITALES DE TRABAJO: ¿ES NECESARIA UNA REGULACIÓN INTERNACIONAL?

LAS PLATAFORMAS DIGITALES DE TRABAJO: ¿ES NECESARIA UNA REGULACIÓN INTERNACIONAL?

In the mid-2000s, Amazon launched its first crowdworking platform as a way to service its growing on-line catalogues. The company found that its computer programmes were unable to distinguish between similar products leading to errors and multiple entries on the Amazon site; it thus needed human labour to correctly tag and classify its catalogue entries. Originally the tasks on the platform were designed for Amazon employees to do in their ‘spare time’, but the company soon realized that it could externalize the tasks to a crowd of workers across the globe, as well as provide a platform for other companies to post tasks. Ironically, it is the failures of artificial intelligence that spurred the need for human input, leading Jeff Bezos, head of Amazon, to aptly describe the Amazon Mechanical Turk (AMT) platform as “artificial-artificial- intelligence” (Irani, 2015). Despite important advances in artificial intelligence, the need for human intelligence to service an ever-ranging array of activities to ensure the smooth functioning of automated or “artificially intelligent” systems continues to grow, with no sign of abating (Corporaal and Lehdonvirta, 2017; Schmidt 2019; Gray and Suri, 2019) 2 .
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25 Lee mas

A review of artificial intelligent approaches applied to part accuracy prediction

A review of artificial intelligent approaches applied to part accuracy prediction

The control of all aspects of product quality is the ultimate technical objective of man- ufacturing industry. In machining operations, this mainly implies workpiece precision. Nowadays, despite the large volume of worldwide academic research on various aspects of metal cutting the control of workpiece precision in industry still relies on the machine-tool operator’s experience and trial and error runs (van Luttervelt and Peng, 1999). The diffi- culties in realising true predictive models to estimate part accuracy in machining arise from the extreme physical phenomena inherent in the process. Machining generates a highly inhomogeneous plastic flow where local stresses generate high rates of plastic deforma- tion that give rise to inhomogeneous thermal fields, high temperatures and pressures. This type of complex plastic flow is difficult to predict even with sophisticated numerical soft- ware (Ivester et al., 2000). These difficulties have forced model development to rely on various levels of empirical input data taken from machining tests in order to model process variables of industrial interest. Recently, the application of mathematical models based on Artificial Intelligence (AI) to learn/acquire the relationships between cutting parameters (e.g. cutting speed, feed rate, depth of cut, lubrication, etc.) and process variables (e.g. cutting force, acoustic emission, sound, vibration, spindle power, cutting temperature, etc.) have been successfully applied and it is expected that their potential use could solve many of the problems encountered in modelling with conventional techniques. Interestingly, in 1998 a CIRP keynote (van Luttervelt et al., 1998) about modelling of machining operations stated the importance of new modelling techniques based on AI. The keynote remarked that in future the application of AI in machining would develop machine-tools with the ability to predict the job quality and take appropriate corrective actions based on sensory feedback. However, present technology is still far away from that goal and the development of a re- liable prediction strategy for predicting part accuracy is a challenge to be met on the way of developing an artificially intelligent and unmanned machine-tool (Risbood, Dixit and Sahasrabudhe, 2003).
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26 Lee mas

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